Resumable Batch Protocol (RBP)

Name: Resumable Batch Protocol (RBP)
Availability: InStock
Author: chomchomchomy

Process large datasets in auditable chunks with JSON progress checkpoints.

Updated Aug 27, 2026

Search Tags:#python #idempotent #checkpoint #data-processing #batch-processing #progress-tracking #resumable

Ready to run

npx skills add https://github.com/chomchomchomy/antigravity-skills --skill resumable-batch-protocol-rbp

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Resumable Batch Protocol (RBP)
Source: https://github.com/chomchomchomy/antigravity-skills/tree/main/resumable_batch_protocol
Command: npx skills add https://github.com/chomchomchomy/antigravity-skills --skill resumable-batch-protocol-rbp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This protocol enables processing large data in small, reliable steps, preserving progress between runs to prevent wasted AI quotas.

Core Features & Use Cases

Chunking: process data in small chunks to reduce per-run load.
Checkpointing: after each item or chunk, write to a progress log.
Idempotency: re-running skips completed items and yields same results.

Quick Start

Process a list of items in small batches, persisting completed IDs after each chunk using ProgressManager.

Frequently Asked Questions about Resumable Batch Protocol (RBP)

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I make batch processing resumable after an interruption in Python?▼

Checkpointing in batch processing records completed items to a JSON progress log after each chunk is processed. When the script restarts, it reloads this state and skips already finished items, ensuring progress is never lost even if the run is interrupted.

What is the best way to process large datasets in small chunks without losing progress?▼

Idempotent batch processing ensures re-running a script skips completed items and yields the exact same results. It achieves this by tracking finished items in a JSON progress log, making long-running data processing tasks safe to re-execute after any interruption without duplicating work.

How do I implement idempotent re-execution for data migration tasks?▼

Idempotent re-execution for data migration tracks completed items in a JSON progress log and skips them on subsequent runs. This guarantees that re-running an interrupted migration yields the same results without duplicating records or wasting processing quotas.

Can I use checkpointing to prevent wasted AI quotas during large data processing?▼

Checkpointing prevents wasted AI quotas by processing data in small chunks and persisting completed IDs to a JSON progress log after each step. If a long-running task is interrupted, the system reloads its state and resumes, avoiding the need to reprocess finished items.

When do I need idempotent checkpointing for batch processing?▼

Idempotent checkpointing is needed for batch processing, data migration, and long-running tasks where interruptions can occur. It ensures that if a process fails mid-run, it can safely resume by skipping completed items tracked in a JSON progress log, preventing data loss and duplicate processing.

Does resumable batch processing work with Python data migration scripts?▼

Resumable batch processing works with Python data migration scripts by chunking data into small, auditable steps and tracking completed items in a JSON progress log. This allows the script to reload state on startup and skip finished items, ensuring idempotent re-execution without data loss.